國立高雄大學圖資館 |

Language: English

Back

Applied survival analysis using R

Moore, Dirk F.

Applied survival analysis using R

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied survival analysis using Rby Dirk F. Moore.
Author:	Moore, Dirk F.
Published:	Cham :Springer International Publishing :2016.
Description:	xiv, 226 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Survival analysis (Biometry)
Online resource:	http://dx.doi.org/10.1007/978-3-319-31245-3
ISBN:	9783319312453$q(electronic bk.)

Applied survival analysis using R
Moore, Dirk F.

Applied survival analysis using R[electronic resource] /by Dirk F. Moore. - Cham :Springer International Publishing :2016. - xiv, 226 p. :ill. (some col.), digital ;24 cm. - Use R!,2197-5736. - Use R!.

Introduction -- Basic Principles of Survival Analysis -- Nonparametric Survival Curve Estimation -- Nonparametric Comparison of Survival Distributions -- Regression Analysis Using the Proportional Hazards Model -- Model Selection and Interpretation -- Model Diagnostics -- Time Dependent Covariates -- Multiple Survival Outcomes and Competing Risks -- Parametric Models -- Sample Size Determination for Survival Studies -- Additional Topics -- References -- Appendix A -- Index -- R Package Index.

Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics. Clearly illustrates concepts of survival analysis principles and analyzes actual survival data using R, in addition to including an appendix with a basic introduction to R Organized via basic concepts and most frequently used procedures, with advanced topics toward the end of the book and in appendices Includes multiple original data sets that have not appeared in other textbooks Dirk F. Moore is Associate Professor of Biostatistics at the Rutgers School of Public Health and the Rutgers Cancer Institute of New Jersey. He received a Ph.D. in biostatistics from the University of Washington in Seattle and, prior to joining Rutgers, was a faculty member in the Statistics Department at Temple University. He has published numerous papers on the theory and application of survival analysis and other biostatistics methods to clinical trials and epidemiology studies.

ISBN: 9783319312453$q(electronic bk.)

Standard No.: 10.1007/978-3-319-31245-3doiSubjects--Topical Terms:

182534
Survival analysis (Biometry)

LC Class. No.: QA276

Dewey Class. No.: 519.546

Applied survival analysis using R
LDR:03839nmm a2200337 a 4500 001 489428
003 DE-He213
005 20161102095651.0
006 m d
007 cr nn 008maaau
008 161213s2016 gw s 0 eng d
020 $a 9783319312453$q(electronic bk.)
020 $a 9783319312439$q(paper)
024 7 $a 10.1007/978-3-319-31245-3 $2 doi
035 $a 978-3-319-31245-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a MBNS $2 bicssc
072 7 $a MED090000 $2 bisacsh
082 0 4 $a 519.546 $2 23
090 $a QA276 $b .M821 2016
100 1 $a Moore, Dirk F. $3 747922
245 1 0 $a Applied survival analysis using R $h [electronic resource] / $c by Dirk F. Moore.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xiv, 226 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Use R!, $x 2197-5736
505 0 $a Introduction -- Basic Principles of Survival Analysis -- Nonparametric Survival Curve Estimation -- Nonparametric Comparison of Survival Distributions -- Regression Analysis Using the Proportional Hazards Model -- Model Selection and Interpretation -- Model Diagnostics -- Time Dependent Covariates -- Multiple Survival Outcomes and Competing Risks -- Parametric Models -- Sample Size Determination for Survival Studies -- Additional Topics -- References -- Appendix A -- Index -- R Package Index.
520 $a Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics. Clearly illustrates concepts of survival analysis principles and analyzes actual survival data using R, in addition to including an appendix with a basic introduction to R Organized via basic concepts and most frequently used procedures, with advanced topics toward the end of the book and in appendices Includes multiple original data sets that have not appeared in other textbooks Dirk F. Moore is Associate Professor of Biostatistics at the Rutgers School of Public Health and the Rutgers Cancer Institute of New Jersey. He received a Ph.D. in biostatistics from the University of Washington in Seattle and, prior to joining Rutgers, was a faculty member in the Statistics Department at Temple University. He has published numerous papers on the theory and application of survival analysis and other biostatistics methods to clinical trials and epidemiology studies.
650 0 $a Survival analysis (Biometry) $3 182534
650 0 $a Failure time data analysis. $3 182533
650 1 4 $a Statistics. $3 182057
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 274067
650 2 4 $a Biostatistics. $3 339693
650 2 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Epidemiology. $3 191577
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Use R! $3 558822
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-31245-3
950 $a Mathematics and Statistics (Springer-11649)